// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "sparse.h"

template<typename Scalar, typename StorageIndex>
void
sparse_vector(int rows, int cols)
{
	double densityMat = (std::max)(8. / (rows * cols), 0.01);
	double densityVec = (std::max)(8. / (rows), 0.1);
	typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
	typedef Matrix<Scalar, Dynamic, 1> DenseVector;
	typedef SparseVector<Scalar, 0, StorageIndex> SparseVectorType;
	typedef SparseMatrix<Scalar, 0, StorageIndex> SparseMatrixType;
	Scalar eps = 1e-6;

	SparseMatrixType m1(rows, rows);
	SparseVectorType v1(rows), v2(rows), v3(rows);
	DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
	DenseVector refV1 = DenseVector::Random(rows), refV2 = DenseVector::Random(rows), refV3 = DenseVector::Random(rows);

	std::vector<int> zerocoords, nonzerocoords;
	initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
	initSparse<Scalar>(densityMat, refM1, m1);

	initSparse<Scalar>(densityVec, refV2, v2);
	initSparse<Scalar>(densityVec, refV3, v3);

	Scalar s1 = internal::random<Scalar>();

	// test coeff and coeffRef
	for (unsigned int i = 0; i < zerocoords.size(); ++i) {
		VERIFY_IS_MUCH_SMALLER_THAN(v1.coeff(zerocoords[i]), eps);
		// VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
	}
	{
		VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
		int j = 0;
		for (typename SparseVectorType::InnerIterator it(v1); it; ++it, ++j) {
			VERIFY(nonzerocoords[j] == it.index());
			VERIFY(it.value() == v1.coeff(it.index()));
			VERIFY(it.value() == refV1.coeff(it.index()));
		}
	}
	VERIFY_IS_APPROX(v1, refV1);

	// test coeffRef with reallocation
	{
		SparseVectorType v4(rows);
		DenseVector v5 = DenseVector::Zero(rows);
		for (int k = 0; k < rows; ++k) {
			int i = internal::random<int>(0, rows - 1);
			Scalar v = internal::random<Scalar>();
			v4.coeffRef(i) += v;
			v5.coeffRef(i) += v;
		}
		VERIFY_IS_APPROX(v4, v5);
	}

	v1.coeffRef(nonzerocoords[0]) = Scalar(5);
	refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
	VERIFY_IS_APPROX(v1, refV1);

	VERIFY_IS_APPROX(v1 + v2, refV1 + refV2);
	VERIFY_IS_APPROX(v1 + v2 + v3, refV1 + refV2 + refV3);

	VERIFY_IS_APPROX(v1 * s1 - v2, refV1 * s1 - refV2);

	VERIFY_IS_APPROX(v1 *= s1, refV1 *= s1);
	VERIFY_IS_APPROX(v1 /= s1, refV1 /= s1);

	VERIFY_IS_APPROX(v1 += v2, refV1 += refV2);
	VERIFY_IS_APPROX(v1 -= v2, refV1 -= refV2);

	VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
	VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));

	VERIFY_IS_APPROX(m1 * v2, refM1 * refV2);
	VERIFY_IS_APPROX(v1.dot(m1 * v2), refV1.dot(refM1 * refV2));
	{
		int i = internal::random<int>(0, rows - 1);
		VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
	}

	VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());

	VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());

	// test aliasing
	VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
	VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
	VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));

	// sparse matrix to sparse vector
	SparseMatrixType mv1;
	VERIFY_IS_APPROX((mv1 = v1), v1);
	VERIFY_IS_APPROX(mv1, (v1 = mv1));
	VERIFY_IS_APPROX(mv1, (v1 = mv1.transpose()));

	// check copy to dense vector with transpose
	refV3.resize(0);
	VERIFY_IS_APPROX(refV3 = v1.transpose(), v1.toDense());
	VERIFY_IS_APPROX(DenseVector(v1), v1.toDense());

	// test conservative resize
	{
		std::vector<StorageIndex> inc;
		if (rows > 3)
			inc.push_back(-3);
		inc.push_back(0);
		inc.push_back(3);
		inc.push_back(1);
		inc.push_back(10);

		for (std::size_t i = 0; i < inc.size(); i++) {
			StorageIndex incRows = inc[i];
			SparseVectorType vec1(rows);
			DenseVector refVec1 = DenseVector::Zero(rows);
			initSparse<Scalar>(densityVec, refVec1, vec1);

			vec1.conservativeResize(rows + incRows);
			refVec1.conservativeResize(rows + incRows);
			if (incRows > 0)
				refVec1.tail(incRows).setZero();

			VERIFY_IS_APPROX(vec1, refVec1);

			// Insert new values
			if (incRows > 0)
				vec1.insert(vec1.rows() - 1) = refVec1(refVec1.rows() - 1) = 1;

			VERIFY_IS_APPROX(vec1, refVec1);
		}
	}
}

EIGEN_DECLARE_TEST(sparse_vector)
{
	for (int i = 0; i < g_repeat; i++) {
		int r = Eigen::internal::random<int>(1, 500), c = Eigen::internal::random<int>(1, 500);
		if (Eigen::internal::random<int>(0, 4) == 0) {
			r = c; // check square matrices in 25% of tries
		}
		EIGEN_UNUSED_VARIABLE(r + c);

		CALL_SUBTEST_1((sparse_vector<double, int>(8, 8)));
		CALL_SUBTEST_2((sparse_vector<std::complex<double>, int>(r, c)));
		CALL_SUBTEST_1((sparse_vector<double, long int>(r, c)));
		CALL_SUBTEST_1((sparse_vector<double, short>(r, c)));
	}
}
